mlsanityData to 13 Oct 2026

On the Minimax Regret for Linear Bandits in a wide variety of Action Spaces

arXiv 2301.03597 ↗First posted 9 Jan 2023cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

As noted in the works of citelattimore2020bandit, it has been mentioned that it is an open problem to characterize the minimax regret of linear bandits in a wide variety of action spaces. In this article we present an optimal regret lower bound for a wide class of convex action spaces.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 1 of its 2 references that are in the map. First posted on arXiv 9 Jan 2023.

Builds on

1 of 2 references.

References · 1

  1. 2022Exploration in Linear Bandits with Rich Action Sets and its Implications for Inferencecited by 9

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Tangential Randomization in Linear Bandits (TRAiL): Guaranteed Inference and Regret Boundscited by 1